So sánh phương pháp

	Suy luận biến phân động (Dynamic Variational Inference)	Monte Carlo Tuần tự
Lĩnh vực	Bayes	Bayes
Họ	Bayesian methods	Bayesian methods
Năm ra đời	2014–2015	1993 (particle filter); 2006 (SMC samplers)
Người khởi xướng	Bayer, Osendorfer, Krishnan and colleagues	Gordon, Salmond & Smith (particle filter); Del Moral, Doucet & Jasra (SMC samplers)
Loại	Bayesian approximate inference	Sequential Bayesian computation
Công trình gốc	Krishnan, R. G., Shalit, U., & Sontag, D. (2015). Deep Kalman Filters. NIPS 2015 Workshop on Advances in Approximate Bayesian Inference.	Gordon, N. J., Salmond, D. J., & Smith, A. F. M. (1993). Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings F - Radar and Signal Processing, 140(2), 107–113.
Tên gọi khác	sequential variational inference, temporal variational inference, variational inference for state-space models, DVI	SMC, particle filter, sequential importance resampling, SMC sampler
Liên quan	6	6
Tóm tắt	Dynamic variational inference extends the variational inference framework to sequential and time-series settings by positing a structured approximate posterior that respects the temporal ordering of latent states. It jointly learns a generative model of how hidden states evolve over time and a recognition network that maps observed sequences back to those latent states, optimising a sequential evidence lower bound (ELBO).	Sequential Monte Carlo (SMC) is a family of simulation-based algorithms that approximate evolving probability distributions by propagating and reweighting a cloud of weighted random draws called particles. It handles nonlinear, non-Gaussian models and streams of data naturally, making it the method of choice for real-time state estimation and posterior approximation over complex distributions.